Informatics Publishing Limited

D. Ashok Kumar ¹, P. Prema ²

Affiliations
1 Department of Computer Science, Government Arts College, Tiruchirappalli, IN
2 Department of Agricultural Economics, Agricultural College and Research Institute, Madurai, IN

Abstract

Apparently weed is a major menace in crop production as it competes with crop for nutrients, moisture, space and light which resulting in poor growth and development of the crop and finally yield. Yield loss accounts for even more than 70% when crops are frown under unweeded condition with severe weed infestation. Weed management is the most significant process in the agricultural applications to improve the crop productivity rate and reduce the herbicide application cost. Existing weed detection techniques does not yield better performance due to the complex background, illumination variation and crop and weed overlapping in the agricultural field image. Hence, there arises a need for the development of effective weed identification technique. To overcome this drawback, this paper proposes a novel Wrapping Curvelet Transformation Based Angular Texture Pattern Extraction Method (WCTATP) for weed identification. In our proposed work, Global Histogram Equalization (GHE) is used improve the quality of the image and Adaptive Median Filter (AMF) is used for filtering the impulse noise from the image. Plant image identification is performed using green pixel extraction and k-means clustering. Wrapping Curvelet transform is applied to the plant image. Feature extraction is performed to extract the angular texture pattern of the plant image. Particle Swarm Optimization (PSO) based Differential Evolution Feature Selection (DEFS) approach is applied to select the optimal features. Then, the selected features are learned and passed through an RVM based classifier to find out the weed. Edge detection and contouring is performed to identify the weed in the plant image. The Fuzzy rule-based approach is applied to detect the low, medium and high levels of the weed patchiness. From the experimental results, it is clearly observed that the accuracy of the proposed approach is higher than the existing Support Vector Machine (SVM) based approaches. The proposed approach achieves better performance in terms of accuracy.

Keywords

Global Histogram Equalization (GHE), Adaptive Median Filter (AMF), Convoluted Gray Level Co-Occurrence Matrix (CGLCM), Wrapping Curvelet Transformation Based Angular Texture Pattern (WCTATP) Extraction Method, Weed Identification.

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N. N. Jothi ¹, E. Prabhakaran ², P. Prema ²

Affiliations
1 Department of Zoology, Presidency College, Chennai-600 005, IN
2 Chellammal Women's College, Chennai-600 032, IN

Abstract

Five lactate dehydrogenase fraction were found in ovarian tissue and they were named LDH1, LDH2, LDH3, LDH4 and LDH5. The activities of these fractions were 11%, 31%, 12%, 17% and 39%, respectively. LDH pattern of ovary showed significant change in the number of the fractions in alloxan induced diabetic mice. There were seven fractions compared to five fractions in control animals. One new fraction appeared between LDH4 and LDH5. The activity of LDHl increased to 18%, LDH2 to 35%, LDH3 to 18%. The activity of other fractions decreased.

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P. Prema ¹, J. Preethi ¹

Affiliations
1 Department of Computer Science and Engineering, Anna University Regional Centre – Coimbatore, IN

Abstract

Koblitz introduced a family of curves which admit especially fast elliptic scalar multiplication. A Koblitz curve is an elliptic curve has convenient features for efficient implementation of elliptic curve cryptography. In order to enable faster computations, scalars need to be reduced and represented using a special base -expansion. Hence an efficient conversion algorithm is indeed which utilize only simple operations such as additions and shifts. It is an attractive class of elliptic curves because they offer faster scalar multiplications. It is also the most demanding operation due to its efficient computation which is widely used in practical cryptosystems.

Keywords

Koblitz Curve, Elliptic Curves, Public-Key Cryptography, Scalar Multiplications, Broadcast Authentication.

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D. Ashok Kumar ¹, P. Prema ²

Affiliations
1 Government Arts College, Trichy, Tamil Nadu, IN
2 Agricultural College and Research Institute, Madurai-625104, Tamil Nadu, IN

Abstract

Texture based weed classification has played an important role in agricultural applications. In the recent years weed classification based on wavelet transform is an effective method. But the feature extraction is main issue for proper classification of weed species. In this paper, the issue of statistical and texture classification based on wavelet transform has been analysed. The efficient texture feature extraction methods are developed for weed discrimination. Three group feature vector can be constructed by the mean and standard deviation of the wavelet statistical features (WSF), Texture feature as Contrast, Cluster Shade, Cluster Prominence and Local Homogeneity (WCSPH) and Energy, Correlation, Cluster Shade, Cluster Prominence and Entropy features (WECSPE) which are derived from the sub-bands of the wavelet decomposition and are used for classification. Experimental results show that Rbio33 Wavelet with WECSPE texture feature obtaining high degree of success rate in classification.

Keywords

Pre-Processing, Wavelets, Texture Features, Neural Network.

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